SEM Focus and Astigmatism Control Using Kernel Image Features
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Solution Overview
Problem
Existing scanning electron microscope (SEM) devices and semiconductor manufacturing devices face inefficiencies due to the time-consuming manual adjustment of focus and astigmatism, which affects the accuracy and speed of electron beam irradiation.
Innovation Solution
A method and device that automatically adjust and optimize focus and astigmatism by using a control unit to change the working distance, obtain original images, extract feature values from kernel images, and control an astigmatism adjuster to improve the lens unit's performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of focus and astigmatism is performed, then the electron beam can be accurately focused on the target, but the process efficiency is reduced due to significant time consumption
Solution Approach 1:
The system performs automatic focus and astigmatism adjustment using image processing algorithms. The control unit automatically acquires images at different focal positions, extracts feature values, calculates optimal focus and astigmatism parameters, and adjusts the lens unit without manual intervention, enabling the system to self-optimize its imaging parameters
Solution Approach 2:
The patent replaces manual mechanical adjustment operations with an automated control system that uses image processing and computational algorithms. The control unit substitutes human operators by automatically analyzing kernel images, extracting feature values, and computing correction parameters for focus and astigmatism
2Measurement precision
If working distance is changed to obtain multiple original images, then focus optimization is improved, but the complexity of the adjustment process increases
Solution Approach 1:
The system extracts kernel images from original images by removing pattern information through image processing. This extraction isolates the focus and astigmatism characteristics in the kernel images, allowing separate optimization of these parameters without being confounded by the sample's structural patterns
Solution Approach 2:
The control unit preliminarily acquires multiple original images at different working distances before performing the actual focus and astigmatism optimization. This preliminary data collection enables subsequent automated analysis and parameter calculation without requiring iterative manual adjustments
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution significantly reduces the time required to optimize focus and astigmatism, enhancing the efficiency of the SEM and semiconductor manufacturing processes by allowing for automatic and simultaneous adjustments.
Implementation Method 1
The scanning coil is configured to generate an electromagnetic field to provide a lens
Implementation Method 2
The electron beam, emitted from an electron beam source, passes through a condenser lens formed by an electromagnetic field and is incident on the target
Data Source
AI summary
A scanning electron microscope (SEM) device includes: an electron beam source configured to emit an electron beam; a lens unit disposed between the electron beam source and a stage configured to seat an object including structures having a pattern is seated, and including a scanning coil, the scanning coil configured to generate an electromagnetic field to provide a lens, and an astigmatism adjuster; and a control unit. The control unit is configured to change a working distance between the lens unit and the object to obtain a plurality of original images, obtain a pattern image, in which the structures appear, and a plurality of kernel images, in which a distribution of the electron beam on the object appears, from the plurality of original images, and control the astigmatism adjuster to adjust the focus and the astigmatism of the lens unit using feature values extracted from the plurality of kernel images.